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📊🛰️ Data processing scripts, ML models, and Explainable AI results created as part of my Masters Thesis @ Johns Hopkins
A Python Package for Computing Effective Precipitation Using Google Earth Engine Climate Data.
Evaluation of extreme hydrometeorological phenomena such as droughts and low water periods, using the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI), in reference to water scarcity, water stress and water availability.
In this repository, two Root Zone Soil Moisture (RZSM) estimation methods are evaluated
Machine Learning based Drought Prediction
Spatiotemporal Analysis of Agricultural Drought Severity and Hotspots in Somaliland. It integrates MODIS-derived vegetation indices and CHIRPS precipitation data to identify and assess drought severity and hotspots over time.
Application of the ARIMA model to forecast rainfall patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahawalnagar District, Punjab, Pakistan.
Lesson materials for Module 2 (M2), "Open Climate Science for Agriculture"
Application of the ETS model to forecast rainfall patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahwalnagar District, Punjab, Pakistan.
A study of the stress response of vegetation to drought situation through multispectral satellite imagery. Case of study of Como lake, summer 2022.
🌍 Monitor climate impacts and assess risks with AgriClime-Sentinel, an advanced platform for real-time atmospheric data and analysis.
Application of the ARIMA model to forecast PET patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahawalnagar District, Punjab, Pakistan.
Computation of 14 Drought Indices (SPI, SPEI, PDSI, RAI, RD, ZSI, EDI, RDDI, PPN, HDSI, CZI, MCZI, PNI, and RDI) and Their Characteristics, Applicable to Daily and Monthly Data from One or More Stations, Using the R Programming Language
A Climate Risk Dashboard for U.S. Agricultural Security
Microwave remote sensing-enabled monitoring of the Amazon forest
a platform designed to provide streamlined, user-friendly, and validated Combined Drought Indicator (CDI) products.
The Big Zip refers to combining data from the Drought Impact Reporter with data from the U.S. Drought Monitor. Datasets are zipped together (joined) by time and place. This produces the dataset accessible via the U.S. Drought Monitor State Impact tool.
This is the highly optimized backbone for a FastAPI Drought Prediction service. It uses a unique load-on-demand strategy with HuggingFace Hub to predict drought occurrence and severity from NASA POWER data. Built to be ridiculously memory-lean, this project runs smoothly on free-tier cloud hosts, proving big predictions don't need big budgets.
This project employs R programming to compute the Standardized Precipitation Evapotranspiration Index (SPEI), a widely used drought index. SPEI calculations were conducted for Bahawalnagar District, Punjab, Pakistan.
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